An examination of sports sponsorship from a small business perspective
Bibliographic record
Abstract
This paper responds to the need for more investigation into the "conceptual underpinnings of sponsorships" (Gardner & Shuman, 1988, p.44) by investigating the spectrum of opportunities that are available to small firms - whether as sports donors or as bona fide sponsors - through the prism of small business Stages of Development theory. A multiple case study approach is employed to explore the nature of sponsorship activities being undertaken by small enterprises and to contribute to the advancement of the authors' 'philanthropy-sponsorship' continuum. This research makes two contributions. First, it presents the classifications of 'patronage' versus 'semistrong sponsorship' versus 'fully functioning sponsorship' relationships, based on the nature of the expected benefits. Second, it evaluates the small business/sports property interface from the perspective of small business phases of development and proposes a framework for linking the small firm to sports sponsorship outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".